_base_ = [
    '../../_base_/models/slowfast_r50.py', '../../_base_/default_runtime.py'
]

load_from = 'https://download.openmmlab.com/mmaction/v1.0/recognition/slowfast/slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb/slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb_20220901-701b0f6f.pth'
dataset_type = 'VideoDataset'
data_root = 'data/venus_tiny/train'
data_root_val = 'data/venus_tiny/val'
ann_file_train = f'data/venus_tiny/venus_tiny_train_cate.txt'
ann_file_val = f'data/venus_tiny/venus_tiny_val_cate.txt'
ann_file_test = f'data/venus_tiny/venus_tiny_val_cate.txt'


file_client_args = dict(io_backend='disk')
train_pipeline = [
    dict(type='DecordInit', **file_client_args),
    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
    dict(type='DecordDecode'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]
val_pipeline = [
    dict(type='DecordInit', **file_client_args),
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=1,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]
test_pipeline = [
    dict(type='DecordInit', **file_client_args),
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=10,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]
BATCH_SIZE = 8
NUM_WORKSERS = 1
train_dataloader = dict(
    batch_size=BATCH_SIZE,
    num_workers=NUM_WORKSERS,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=dict(video=data_root),
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=BATCH_SIZE,
    num_workers=NUM_WORKSERS,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=dict(video=data_root_val),
        pipeline=val_pipeline,
        test_mode=True))
test_dataloader = dict(
    batch_size=1,
    num_workers=NUM_WORKSERS,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        data_prefix=dict(video=data_root_val),
        pipeline=test_pipeline,
        test_mode=True))

# model settings
model = dict(
    data_preprocessor=dict(
        type='ActionVenusDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        format_shape='NCTHW'))

val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=64, val_begin=1, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=1e-4),
    clip_grad=dict(max_norm=40, norm_type=2))

param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.1,
        by_epoch=True,
        begin=0,
        end=18,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=64,
        eta_min=0,
        by_epoch=True,
        begin=0,
        end=64)
]

default_hooks = dict(
    checkpoint=dict(interval=4, max_keep_ckpts=3), logger=dict(interval=100))
